Lecturer’s desk

1. Screen Cabinet Cabinet Lecturer’s desk Table Computer Storage Cabinet Row A 3 4 5 19 6 18 7 17 16 8 15 9 10 11 14 13 12 Row B 1 2 3 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row C 1 2 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row D 1 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row E 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row F 27 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 28 Row G 27 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 29 10 19 11 18 16 15 13 12 17 14 28 Row H 27 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row I 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 1 Row J 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 28 27 1 Row K 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row L 20 1 19 2 18 3 17 4 16 5 15 6 7 14 13 INTEGRATED LEARNING CENTER ILC 120 9 8 10 12 11 broken desk

2. Guest Lecture – Nick ThorneIntroduction to Statistics for the Social SciencesSBS200, COMM200, GEOG200, PA200, POL200, or SOC200Lecture Section 001, Spring, 2014Room 120 Integrated Learning Center (ILC)10:00 - 10:50 Mondays, Wednesdays & Fridays. Welcome http://www.youtube.com/watch?v=oSQJP40PcGI

3. Please click in My last name starts with a letter somewhere between A. A – D B. E – L C. M – R D. S – Z

4. Lab sessions Labs continue this week with Project 2

8. Create example of t-test Identify single IV (two levels) Identify DV (must be numeric) Graph should have two bars (one for each mean) Think about how you might Study Type 2: t-test Comparing Two Means? Use a t-test

9. Schedule of readings Before next exam (April 11th) Please read chapters 7 – 11 in Ha & Ha Please read Chapters 2, 3, and 4 in Plous Chapter 2: Cognitive Dissonance Chapter 3: Memory and Hindsight Bias Chapter 4: Context Dependence

10. Homework due – Wednesday (April 2nd) On class website: Please print and complete homework worksheet #15 Hypothesis testing

11. Use this as your study guide By the end of lecture today3/28/14 Review what makes a study a t-test Logic of hypothesis testing Steps for hypothesis testing Levels of significance (Levels of alpha) what does p < 0.05 mean? what does p < 0.01 mean?

12. Gender . Type I or type II error? Independent Variable? Height Dependent Variable? IV: Nominal IV: Nominal Ordinal Interval or Ratio? Who is taller men or women? DV: Ratio DV: Nominal Ordinal Interval or Ratio? IV: Discrete IV: Continuous or discrete? What would null hypothesis be? DV: Continuous DV: Continuous or discrete? No difference in the height between men and women

13. . Type I or type II error? Two –tailed One-tailed Or Two –tailed? Between Between Or within? Who is taller men or women? Quasi Quasi or True? What would null hypothesis be? No difference in the height between men and women

14. . Type I or type II error? Who is taller men or women? What would null hypothesis be? No difference in the height between men and women Type I error: Rejecting a true null hypothesis Type I Error Saying that there is a difference in height when in fact there is not (false alarm) Type II error: Not rejecting a false null hypothesis Type II Error Saying there is no difference in height when in fact there is a difference (miss) This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA t-test

15. . Type I or type II error? Curly versus straight hair – which is more “dateable”? What would null hypothesis be? No difference in the dateability between curly and straight hair Type I error: Rejecting a true null hypothesis Saying that there is a difference in dateability when in fact there is not (false alarm) Type II error: Not rejecting a false null hypothesis Saying there is no difference in dateability when in fact there is a difference (miss) This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA t-test

16. Writing Assignment Please watch this video describing a series of t-tests What is the independent variable? How many different dependent variables did they use? (They would conduct a different t-test for every dependent variable) http://www.everydayresearchmethods.com/2011/09/curly-or-straight-.html http://www.youtube.com/watch?v=z7kfiA2SXMY http://www.youtube.com/watch?v=n4WQhJHGQB4

17. Writing Assignment Worksheet Design two t-tests http://www.youtube.com/watch?v=z7kfiA2SXMY http://www.youtube.com/watch?v=n4WQhJHGQB4

18. Five steps to hypothesis testing Step 1: Identify the research problem (hypothesis) Describe the null and alternative hypotheses How is a t score same as a z score? How is a t score different than a z score? Step 2: Decision rule • Alpha level? (α= .05 or .01)? • Critical statistic (e.g. z or t) value? Step 3: Calculations Step 4: Make decision whether or not to reject null hypothesis If observed z (or t) is bigger then critical z (or t) then reject null Population versus sample standard deviation Population versus sample standard deviation Step 5: Conclusion - tie findings back in to research problem

19. . . A note on z scores, and t score: • Numerator is always distance between means • (how far away the distributions are or “effect size”) • Denominator is always measure of variability • (how wide or much overlap there is between distributions) Difference between means Difference between means Variability of curve(s)(within group variability) Variabilityof curve(s)

20. . A note on variability versus effect size Difference between means Difference between means Variability of curve(s)(within group variability) Variabilityof curve(s)

21. . A note on variability versus effect size Difference between means Difference between means . Variability of curve(s)(within group variability) Variabilityof curve(s)

22. . Effect size is considered relativeto variability of distributions 1. Larger variance harder to find significant difference Treatment Effect x Treatment Effect 2. Smaller variance easier to find significant difference x

23. . Effect size is considered relativeto variability of distributions Treatment Effect x Difference between means Treatment Effect x Variability of curve(s)(within group variability)

24. Thank you! See you next time!!